You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/02/09 23:56:41 UTC
[jira] [Assigned] (SPARK-19538) DAGScheduler and TaskSetManager can
have an inconsistent view of whether a stage is complete.
[ https://issues.apache.org/jira/browse/SPARK-19538?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-19538:
------------------------------------
Assignee: Apache Spark (was: Kay Ousterhout)
> DAGScheduler and TaskSetManager can have an inconsistent view of whether a stage is complete.
> ---------------------------------------------------------------------------------------------
>
> Key: SPARK-19538
> URL: https://issues.apache.org/jira/browse/SPARK-19538
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 2.1.0
> Reporter: Kay Ousterhout
> Assignee: Apache Spark
>
> The pendingPartitions in Stage tracks partitions that still need to be computed, and is used by the DAGScheduler to determine when to mark the stage as complete. In most cases, this variable is exactly consistent with the tasks in the TaskSetManager (for the current version of the stage) that are still pending. However, as discussed in SPARK-19263, these can become inconsistent when an ShuffleMapTask for an earlier attempt of the stage completes, in which case the DAGScheduler may think the stage has finished, while the TaskSetManager is still waiting for some tasks to complete (see the description in this pull request: https://github.com/apache/spark/pull/16620). This leads to bugs like SPARK-19263. Another problem with this behavior is that listeners can get two StageCompleted messages: once when the DAGScheduler thinks the stage is complete, and a second when the TaskSetManager later decides the stage is complete. We should fix this.
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org